Last modified 2025-10-19 |
Create Visualization Templates (Abstractions)
 | Abbreviations Key |
| HISE | Human Immune System Explorer |
| IDE | integrated development environment |
| SDK | software development kit |
| TEA-seq | transcripts, epitomes, and accessibility sequencing |
You can create a visualization template, or abstraction, within a HISE NextGen IDE. An abstraction acts as a visualization template in its initial state, devoid of any data. It transforms into a fully-functional visualization application only when data is introduced into the abstraction. Much like hp.save_dash_app(), the creator of the abstraction is required to develop an app.py file that outlines the visualization's layout. Once the app.py script is developed, simply call hp.save_abstraction() to save your visualization template to HISE. Once saved, any users with access to the same account can stream their own chosen datasets into an abstraction and generate an interactive visualization application.
These visualization templates are stored at the account level. This ensures that all projects possess the capability to generate visualizations tailored to their specific datasets.
Save an Abstraction
You can save a visualization as an abstraction in HISE so that others can use the saved abstraction with their own samples to generate visualization applications.
Choose result file type
Select the result file type for which you want to create an interactive visualization. For example, you might choose a visualization that pertains to demographic or sample metadata, such as lab results, or one associated with an automated pipeline, such as fixed RNA. This step is crucial because an abstraction designed for a specific dataset, such as Olink, can't be utilized to visualize, say, TEA-seq data. For a list of available result file types, see Query SDK fileType.
Additionally, give careful consideration to the data structure, especially the column names of a data.frame, during the abstraction creation process. Abstractions that exclusively read data directly from HISE adhere to standardized column names. However, the SDK allows you to define custom column names. Analytical processes can generate any number of new columns.
Create app.py script
After identifying the data type, develop an app.py script that outlines the layout and interactive components of the visualization. Creating an abstraction is straightforward—all you need is a bug-free app.py script. However, the code structure within app.py varies based on the chosen visualization framework. For instance, the code to generate two tabs with scatterplots will differ between a Dash application and Vitessce. Check the app.py scripts in the IDE environment for various visualization frameworks you can use for reference.
Use the HISE SDK
To store the visualization template in HISE, open a notebook with a Python kernel, import the hisepy module, and invoke hisepy.save_abstraction(). This method requires the filepath to the app.py script, along with additional information, such as a description, title, and specification of the result file type for which the abstraction is intended.
Related Resources
Use the HISE SDK to Create Visualizations (Tutorial)